

import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


__snake_case				:      Union[str, Any]   		=			getLogger(__name__)


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__  ,	a__ = 8  ,	a__ = 1_0_2_4  ,	a__="val"  ,	a__=None  ,	a__=False  ,	a__="summarization"  ,	a__=None  ,	a__=1  ,	a__ = None  ,	a__=""  ,	**a__  ,	):

			'''simple docstring'''



			a_						:						Any 							= str(a__)
			assert local_rank is not None
			torch.distributed.init_process_group(backend="""nccl"""  ,	rank=a__)

			a_						:						Tuple 							= Path(a__)
			a_						:						Optional[Any] 							= save_dir.joinpath(f'''rank_{local_rank}_output.json''')
			torch.cuda.set_device(a__)
			a_						:						List[str] 							= AutoModelForSeqaSeqLM.from_pretrained(a__).cuda()
			if fpaa:
						a_						:						Optional[Any] 							= model.half()
			# determine if we need to increase num_beams
			use_task_specific_params(a__  ,	a__)  # update config with task specific params
			a_						:						List[str] 							= generate_kwargs.pop("""num_beams"""  ,	model.config.num_beams)  # AttributeError risk?
			if num_return_sequences > num_beams:
						a_						:						Tuple 							= num_return_sequences

			a_						:						List[Any] 							= AutoTokenizer.from_pretrained(a__)
			logger.info(f'''Inferred tokenizer type: {tokenizer.__class__}''')  # if this is wrong, check config.model_type.

			if max_source_length is None:
						a_						:						str 							= tokenizer.model_max_length
			if prefix is None:
						a_						:						str 							= prefix or getattr(model.config  ,	"""prefix"""  ,	"""""") or """"""
			a_						:						Optional[int] 							= SeqaSeqDataset(
			    a__  ,	a__  ,	a__  ,	max_target_length=1_0_2_4  ,	type_path=a__  ,	n_obs=a__  ,	prefix=a__  ,	**a__  ,	)
			# I set shuffle=True for a more accurate progress bar.
			# If all the longest samples are first, the prog bar estimate is too high at the beginning.
			a_						:						Optional[int] 							= ds.make_sortish_sampler(a__  ,	distributed=a__  ,	add_extra_examples=a__  ,	shuffle=a__)
			a_						:						Dict 							= DataLoader(a__  ,	sampler=a__  ,	batch_size=a__  ,	collate_fn=ds.collate_fn)
			a_						:						Dict 							= []
			for batch in tqdm(a__):
						a_						:						Any 							= model.generate(
						    input_ids=batch["""input_ids"""].to(model.device)  ,	attention_mask=batch["""attention_mask"""].to(model.device)  ,	num_return_sequences=a__  ,	num_beams=a__  ,	**a__  ,	)
						a_						:						List[str] 							= tokenizer.batch_decode(a__  ,	skip_special_tokens=a__  ,	clean_up_tokenization_spaces=a__)
						a_						:						Union[str, Any] 							= batch["""ids"""]
						if num_return_sequences > 1:
									a_						:						int 							= chunks(a__  ,	a__)  # batch size chunks, each of size num_return_seq
						for i, pred in enumerate(a__):
									results.append({"""pred""": pred, """id""": ids[i].item()})
			save_json(a__  ,	a__)
			return results, sampler.num_replicas


def 					_UpperCAmelCase				(						):

			'''simple docstring'''



			a_						:						int 							= argparse.ArgumentParser(
			    epilog="""Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate""")
			parser.add_argument("""--data_dir"""  ,	type=a__  ,	help="""like cnn_dm/test.source""")
			parser.add_argument(
			    """--model_name"""  ,	type=a__  ,	help="""like facebook/bart-large-cnn,t5-base, etc."""  ,	default="""sshleifer/distilbart-xsum-12-3"""  ,	)
			parser.add_argument("""--save_dir"""  ,	type=a__  ,	help="""where to save"""  ,	default="""tmp_gen""")
			parser.add_argument("""--max_source_length"""  ,	type=a__  ,	default=a__)
			parser.add_argument(
			    """--type_path"""  ,	type=a__  ,	default="""test"""  ,	help="""which subset to evaluate typically train/val/test""")
			parser.add_argument("""--task"""  ,	type=a__  ,	default="""summarization"""  ,	help="""used for task_specific_params + metrics""")
			parser.add_argument("""--bs"""  ,	type=a__  ,	default=8  ,	required=a__  ,	help="""batch size""")
			parser.add_argument(
			    """--local_rank"""  ,	type=a__  ,	default=-1  ,	required=a__  ,	help="""should be passed by distributed.launch""")

			parser.add_argument(
			    """--n_obs"""  ,	type=a__  ,	default=a__  ,	required=a__  ,	help="""How many observations. Defaults to all.""")
			parser.add_argument(
			    """--num_return_sequences"""  ,	type=a__  ,	default=1  ,	required=a__  ,	help="""How many sequences to return""")
			parser.add_argument(
			    """--sync_timeout"""  ,	type=a__  ,	default=6_0_0  ,	required=a__  ,	help="""How long should master process wait for other processes to finish."""  ,	)
			parser.add_argument("""--src_lang"""  ,	type=a__  ,	default=a__  ,	required=a__)
			parser.add_argument("""--tgt_lang"""  ,	type=a__  ,	default=a__  ,	required=a__)
			parser.add_argument(
			    """--prefix"""  ,	type=a__  ,	required=a__  ,	default=a__  ,	help="""will be added to the begininng of src examples""")
			parser.add_argument("""--fp16"""  ,	action="""store_true""")
			parser.add_argument("""--debug"""  ,	action="""store_true""")
			a_						:						Union[str, Any] 							= time.time()
			a_							,					a_						:						Optional[Any] 							= parser.parse_known_args()
			a_						:						int 							= parse_numeric_n_bool_cl_kwargs(a__)
			if generate_kwargs and args.local_rank <= 0:
						print(f'''parsed the following generate kwargs: {generate_kwargs}''')
			a_						:						str 							= Path(args.save_dir + """_tmp""")
			Path(a__).mkdir(exist_ok=a__)  # this handles locking.
			a_						:						str 							= list(json_save_dir.glob("""rank_*.json"""))
			if intermediate_files:
						raise ValueError(f'''Found files at {json_save_dir} please move or remove them.''')
						# In theory, a node could finish and save before another node hits this. If this happens, we can address later.
			a_						:						Dict 							= {}
			if args.src_lang is not None:
						a_						:						Dict 							= args.src_lang
			if args.tgt_lang is not None:
						a_						:						str 							= args.tgt_lang

			Path(args.save_dir).mkdir(exist_ok=a__)
			a_							,					a_						:						Tuple 							= eval_data_dir(
			    args.data_dir  ,	a__  ,	args.model_name  ,	type_path=args.type_path  ,	bs=args.bs  ,	fpaa=args.fpaa  ,	task=args.task  ,	local_rank=args.local_rank  ,	n_obs=args.n_obs  ,	max_source_length=args.max_source_length  ,	num_return_sequences=args.num_return_sequences  ,	prefix=args.prefix  ,	dataset_kwargs=a__  ,	**a__  ,	)

			if args.local_rank <= 0:
						a_						:						List[str] 							= Path(args.save_dir)
						save_dir.mkdir(exist_ok=a__)
						a_						:						Tuple 							= gather_results_from_each_node(a__  ,	a__  ,	args.sync_timeout)
						a_						:						str 							= combine_partial_results(a__)
						if args.num_return_sequences > 1:
									a_						:						str 							= save_dir.joinpath("""pseudolabel_results.json""")
									print(f'''Saving aggregated results at {save_path}, intermediate in {json_save_dir}/''')
									save_json(a__  ,	a__)
									return
						a_						:						Any 							= Path(args.data_dir).joinpath(args.type_path + """.target""")
						with open(a__) as f:
									a_						:						Optional[int] 							= [x.rstrip() for x in f.readlines()][: len(a__)]

						# Calculate metrics, save metrics,  and save _generations.txt
						a_						:						Any 							= """translation""" in args.task
						a_						:						Optional[int] 							= calculate_bleu if calc_bleu else calculate_rouge
						a_						:						Union[str, Any] 							= """bleu""" if calc_bleu else """rouge"""
						a_						:						Dict 							= score_fn(a__  ,	a__)
						a_						:						Optional[Any] 							= len(a__)
						a_						:						Tuple 							= time.time() - start_time
						a_						:						Optional[int] 							= round(runtime / metrics["""n_obs"""]  ,	4)
						a_						:						List[str] 							= num_replicas
						# TODO(@stas00): add whatever metadata to metrics
						a_						:						int 							= save_dir.joinpath(f'''{args.type_path}_{metric_name}.json''')
						save_json(a__  ,	a__  ,	indent=a__)
						print(a__)
						write_txt_file(a__  ,	save_dir.joinpath(f'''{args.type_path}_generations.txt'''))
						if args.debug:
									write_txt_file(a__  ,	save_dir.joinpath(f'''{args.type_path}.target'''))
						else:
									shutil.rmtree(a__)


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						Union[str, Any] 							= []
			for partial_result in partial_results:
						records.extend(a__)
			a_						:						Any 							= sorted(a__  ,	key=lambda a__: x["id"])
			a_						:						Optional[int] 							= [x["""pred"""] for x in records]
			return preds


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__):

			'''simple docstring'''



			a_						:						List[Any] 							= time.time()
			logger.info("""waiting for all nodes to finish""")
			a_						:						Optional[int] 							= None
			while (time.time() - start_wait) < timeout:
						a_						:						Union[str, Any] 							= list(save_dir.glob("""rank_*.json"""))
						if len(a__) < num_replicas:
									continue
						try:
									# make sure all json files are fully saved
									a_						:						Tuple 							= lmap(a__  ,	a__)
									return json_data
						except JSONDecodeError:
									continue
			else:
						raise TimeoutError("""Rank 0 gave up on waiting for other processes""")
			# Unreachable


if __name__ == "__main__":
	# Usage for MT:
	run_generate()





from __future__ import annotations


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			if len(a__) == 0:
						return []
			a_							,					a_						:						List[Any] 							= min(a__), max(a__)
			a_						:						Tuple 							= int(max_value - min_value) + 1
			a_						:						list[list] 							= [[] for _ in range(a__)]

			for i in my_list:
						buckets[int(i - min_value)].append(a__)

			return [v for bucket in buckets for v in sorted(a__)]


if __name__ == "__main__":
	from doctest import testmod

	testmod()
	assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
	assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]


